Periodic Labs co-founder Liam Fedus assesses timelines for autonomous machine self-improvement with Elad Gil.
Prediction Not checkable as stated
Fedus: Physical sciences and engineering will follow machine learning scaling laws
“And I think the physical sciences, physical engineering, Will have a very similar property where we establish these scaling properties and Bring that mindset.”
Insight
Fedus: Intelligence is not scalar; AI shows extreme, non-intuitive spikiness
“I think one fallacy is thinking about intelligence as a scalar. We've consistently seen these systems have a very odd spikiness, and it's actually possible to architect a system that is world-class on some math domain, but then you could do some perturbations …”
Prediction Not checkable as stated
Fedus: Self-improving software AI will not automatically generalize to domains like biology
“So rolling forward that software engineering self-improvement, I think you're going to have a system that can write complete repositories, identify bugs, refactor code, But it doesn't suddenly understand biology. Right? It's just like there's a domain gap ther…”
Assertion Not checkable as stated
Fedus: John Schulman steered OpenAI toward a general chatbot over narrow tools
“And we're all spitballing ideas, like writing bot coding bot. You know, very natural at the time. Some of our least interesting ideas were a meeting bot, so it would just sit in a Google Meet, take notes, and then send out, like, to-dos after. But John Schulma…”
Opinion
Fedus: AI must connect to the physical world to accelerate science
“The opinion that I and others held as periodic was, you're not going to see the same kind of acceleration in science and technology unless you start connecting these things to the physical world.”
Insight
Fedus: Training materials AI on academic literature fails to find ground truth
“One of the engineers on our team was looking at a reported material property and It was just sort of extracted values from literature, and it was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on th…”